Treat code from an unrestricted AI model as untrusted until it has passed independent human review and your normal security checks. “Unrestricted” describes what the tool is allowed to do—such as read files, run commands, access networks, or modify a repository—not whether every line it generates is vulnerable. Control both risks: defects in the code and the agent’s ability to affect your systems.
What “unrestricted” means—and what it does not
An AI coding tool can range from a text assistant that suggests a patch to an agent that reads a repository, runs shell commands, installs packages, and commits changes. The more autonomy and access it has, the more important it is to constrain its runtime. That is separate from reviewing the code it produces: a tightly sandboxed agent can still suggest vulnerable code, and a sound patch can still be mishandled by an over-privileged agent.
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Official guidance provides control recommendations, not a universal defect rate proving that AI-generated code is less secure than human-written code. Apply the same secure-development gates to all changes, then add safeguards for the agent’s access and for especially sensitive code paths.
Set boundaries before the agent starts
Choose approved tools, data, and operations
Write a usage policy covering approved tools and tasks, information that must not be sent to third-party services, and prohibited operations. Do not put credentials or sensitive files into prompts or other context supplied to a tool. A coding assistant may send more project context than the currently visible file, and .gitignore does not prevent a tool from reading local files. Use the tool’s context-exclusion controls for secrets; where policy requires it, use an approved self-hosted or enterprise arrangement. See the OWASP Secure Coding with AI Cheat Sheet.
#1 Best Overall
Limit agent permissions and reachable systems
Before enabling agentic actions, place the tool in a sandboxed development container, restricted shell, virtual machine, or ephemeral workspace. Give it only the filesystem access and commands needed for the task; block unnecessary outbound network access; and use task-scoped credentials. Keep production credentials, SSH keys, and organization secrets outside its reach. Avoid auto-accepting operations in unfamiliar or untrusted repositories. OWASP’s AI coding guidance describes these containment controls.
Review the actual change before accepting it
Require an accountable, independent human
Review the diff, not just the AI’s explanation or a generated review. OWASP’s AI Security Verification Standard (AISVS), Appendix C, control AC.4.1 calls for review by a qualified human engineer other than the identity that requested the generation; the AI agent does not count as that reviewer. Assign a developer responsibility for the change’s security and maintainability, and preserve who requested, reviewed, approved, and shipped it.
Inspect high-risk changes in the diff
Look for unexpected files, scope changes without a clear reason, new dependencies, network calls, shell execution, exposed secrets, weakened tests, and altered authorization or input-validation behavior. Give heightened scrutiny to authentication, authorization, cryptography, identity and access management (IAM), CI/CD, deployment, and sandbox or network-policy changes. Also inspect package installation scripts, CI workflows, Dockerfiles, build configuration, and deployment manifests: these can run automatically or in privileged contexts.
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Rank #3
Run layered security checks on every applicable change
Run the repository’s security pipeline on pull requests whether the code was written by a person or an AI. Select checks that fit the application and infrastructure:
- Static analysis (SAST): find suspicious code patterns without running the application.
- Software composition analysis (SCA): inspect dependencies for known risks and review newly introduced packages.
- Secret scanning: detect credentials or tokens accidentally included in code or configuration.
- Infrastructure-as-code scanning: check infrastructure and deployment definitions for insecure settings.
- Dynamic and interactive analysis (DAST/IAST): test a running application where the pipeline and application support it.
Set severity thresholds in policy and make critical findings merge-blocking. AISVS AC.4.2 gives CVSS >= 9.0 as an example threshold for blocking a merge; an organization may instead use its equivalent severity threshold. Any bypass should require a written, human-approved exception. Scanners need human triage, and their language and framework coverage, CI integration, blocking behavior, and false-positive handling should be evaluated in the context of your own pipeline; the guidance does not establish a universally best vendor or configuration. See OWASP AISVS.
Test security behaviors scanners may miss
Write adversarial tests independently of the generation step. Exercise malformed and invalid inputs, boundary conditions, expired credentials, concurrent access, authorization decisions, and unsafe deserialization. For security-critical input validation, authorization, and deserialization, AISVS AC.4.5 calls for differential fuzzing or property-based tests.
NIST’s IR 8397, published October 6, 2021, recommends a broader verification menu including threat modeling, static scanning, hardcoded-secret checks, black-box and structural tests, fuzzing, web application scanners where applicable, and review of included code. It is general software verification guidance, not a measured study of AI-generated code. A passing test suite only shows that the behaviors those tests assert worked under the tested conditions; AI-generated tests and green pass rates alone are not proof of security.
Best Value
Keep untrusted prompts and pull requests away from CI secrets
Issue text, pull-request descriptions, comments, and diffs can be attacker-controlled when an agent reads them. Constrain or sanitize that context, isolate CI agents, and grant only the minimum job-specific access. A review bot should not receive deploy keys or secrets it does not need; an agent acting on untrusted repository content should not have broad CI secrets or write privileges. Keep a way to revoke credentials or pause the agent, and maintain useful logs of its actions and access.
Respond to findings and possible credential exposure
- Stop the change from advancing: block merge or deployment when a required check fails, unless an authorized exception is recorded.
- Triage and record the finding: determine the affected code, dependencies, configuration, and systems, and assign an accountable owner.
- Remediate and verify: fix the underlying issue, then rerun the relevant security checks and tests before reconsidering the change.
- If credentials may have been exposed: revoke or rotate them and investigate the systems the agent could reach and its outbound activity, following your organization’s incident-response plan.
These steps should fit the organization’s existing response process; the cited guidance supports restricted credentials, logging, and merge gates rather than prescribing one universal incident procedure.
Record provenance and ownership
Keep an audit trail that makes the change attributable: who requested it, who reviewed and approved it, what tool or model version was used where feasible, and how it progressed from suggestion to commit and deployment. Human ownership is not replaced by tool logs; the developer approving the change remains accountable for its security and maintainability. OWASP’s Secure Coding with AI Cheat Sheet and AISVS support human accountability and review controls.
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NIST SP 800-218A, published July 26, 2024, is a secure-development profile for generative AI and dual-use foundation-model development. It is intended to be used with NIST SSDF 1.1, so treat it as a framework companion rather than assuming every clause directly governs arbitrary code produced by an assistant. OWASP’s AI coding and DevSecOps guidance is maintained and can change; consult the current version when setting controls.
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